One of the most human-readable representations of a visual scene is the semantic scene graph: if it is present, the task of generating the text describing the scene is trivial[1]}. The nodes of such a graph are usually nouns that name objects on the stage. The node can be assigned its coordinates on the image.
There are approaches to generate a scene graph using deep learning techniques. The first step of the algorithm is usually to select areas of the image in which the presence of objects is likely [2]} [3]}[4]}. Such regions are searched using a separate convolutional network, for example, Faster R-CNN[5]}, which returns the coordinates of the region of interest and the corresponding feature tensor.
However, with this approach, graph representations arise not at every level of abstraction, but only at the highest, i.e. at the level of whole objects, but not at the level of their parts. Before the appearance of this graph, intermediate scene representations are activation tensors that arise in a neural network (or a group of neural networks), often convolutional.
